Recently, bandit algorithms have found practical application in areas from dynamic pricing [1]} and healthcare [2]} to finance [3]} and recommender systems [4]}, and many more. For many of these application areas, generalised linear bandit algorithms are among the most commonly used approaches, as they are able to capture the structure of the rewards and actions often seen in practice.
Additionally, there exist algorithms with near-optimal theoretical guarantees for this problem [5]}, [6]}, [7]}. However, these algorithms require the learner receives feedback from the environment immediately. This strict requirement for immediate rewards often goes unmet in practice. Consequently, we consider learning the stochastic generalised linear bandit under delayed feedback.
